A Game-Theoretic Perspective on Disease-Modifying Therapy Choice in Resource-Limited Settings

Decision-making around disease-modifying therapy (DMT) in chronic neurological disorders is often framed as a linear optimization problem driven by efficacy and safety data. In practice, especially in resource-limited settings, therapy choice represents a strategic interaction among multiple agents, including patients, clinicians, health systems, and disease dynamics operating under uncertainty, constraints, and competing objectives. In this work, a game-theoretic perspective to formalize DMT selection as a structured decision space rather than a single optimal choice. Using simplified strategic models, trade-offs between efficacy, toxicity, affordability, adherence, and long-term disease control can be represented as equilibria emerging from interacting incentives rather than fixed hierarchies of outcomes. The framework highlights how rational local decisions may lead to globally suboptimal trajectories, and how dominance, cooperation, and delayed commitment can shape therapeutic pathways over time. This approach provides a conceptual tool for exploring strategy spaces and identifying conditions under which particular choices become stable or fragile. By making implicit clinical reasoning explicit, the framework offers a basis for hypothesis generation, policy discussion, and future quantitative modeling of treatment strategies in constrained healthcare environments.